Search

Background The description of the three-dimensional (3D) trajectory of the body center of mass (BCoM) provides useful insights on the mechanics of locomotion. The BCoM trajectory can be estimated from ground reaction forces, recorded by force platforms (GRF, gold standard), or from marker trajectories recorded by stereophotogrammetric systems (MKR). However, both instruments do not allow for monitoring locomotion in the real-life environment. In this perspective, magneto-inertial measurement units (MIMUs) are particularly attractive being wearable, thus enabling to collect movement data out of the laboratory. Research questions To investigate the feasibility and accuracy of a recent marketed full-body MIMU-based method for the estimation of the 3D BCoM trajectory and energetics during walking. Methods Twelve subjects walked at self-selected and slow speed along a 12 m long walkway. GRF and MKR were acquired using three force platforms and a stereophotogrammetric system. MIMU data were collected using a full-body MIMU-based motion capture system (Xsens MTw Awinda). The 3D BCoM trajectory, external mechanical work and energy recovery were extracted from the data acquired by the three measurement systems, using state-of-the-art methods. The accuracy of both MKR- and MIMU-based estimates compared with GRF was assessed for the BCoM trajectory (maximum, minimum, range, and RMSD), as well as for mechanical work and energy recovery. Results A total number of 108 strides were analyzed. MIMU-based BCoM trajectory displayed larger errors in comparison with GRF (and MKR) for the trajectory ranges: 89 ± 47(93 ± 44)% in antero-posterior, 46 ± 25(40 ± 79)% medio-lateral and -13 ± 23(-5 ± 25)% vertical directions, leading to a 3D RMSD of 17 ± 5(12 ± 5) mm (mean ± SD). These discrepancies largely affected the estimation of both mechanical work and energy recovery (+115 ± 85% and -28 ± 21%, respectively). Significance Preliminary findings highlighted that the tested MIMU-based method for BCoM trajectory estimation still lacks accuracy and that the quantification of energetics in real-life situations remains an open challenge.

Objective quantitative evaluation of motor skill development is of increasing importance to carefully drive physical exercise programs in childhood. Running is a fundamental motor skill humans adopt to accomplish locomotion, which is linked to physical activity levels, although the assessment is traditionally carried out using qualitative evaluation tests. The present study aimed at investigating the feasibility of using inertial sensors to quantify developmental differences in the running pattern of young children. Qualitative and quantitative assessment tools were adopted to identify a skill-sensitive set of biomechanical parameters for running and to further our understanding of the factors that determine progression to skilled running performance. Running performances of 54 children between the ages of 2 and 12 years were submitted to both qualitative and quantitative analysis, the former using sequences of developmental level, the latter estimating temporal and kinematic parameters from inertial sensor measurements. Discriminant analysis with running developmental level as dependent variable allowed to identify a set of temporal and kinematic parameters, within those obtained with the sensor, that best classified children into the qualitative developmental levels (accuracy higher than 67%). Multivariate analysis of variance with the quantitative parameters as dependent variables allowed to identify whether and which specific parameters or parameter subsets were differentially sensitive to specific transitions between contiguous developmental levels. The findings showed that different sets of temporal and kinematic parameters are able to tap all steps of the transitional process in running skill described through qualitative observation and can be prospectively used for applied diagnostic and sport training purposes.

Main topics: Technical developments in movement science; Mathematical simulation in human mov. science Introduction and aim: The determination of the accurate 3D orientation of a body segment is a crucial aspect in clinical movement analysis [1]. Among motion capture technologies, wearable sensors, suchasmagnetic-inertialmeasurementunits (MIMUs), are gaining popularity. Several methods were developed to estimate 3D orientation from MIMU data, in order to effectively exploit the complementary properties of inertial (gyroscopes and accelerometers) and magnetic sensors embedded in the MIMUs [2]. It is well known that these sensors are affected by several error sources, including measurement noise, bias instability, calibration errors, ferromagneticdisturbances, external accelerations [3]. Thepurpose of the present study is to assess the contribution of the sources of error associated to each sensor to the uncertainty affecting the 3D orientation estimation, during a consolidated clinical test. Patients/materials andmethods:ATimedUpandGo test (TUG) was performed by ten healthy subjects (5M, 5 F, 28 5 y). OneMIMU (Opal, APDM Inc.) was secured to the lower back of each subject and angular velocity, acceleration and local magnetic field vector data were collected. The trajectories of four markers attached to theMIMUwere trackedby a stereophotogrammetric system (Vicon MX3), and the orientation of a marker-based frame was obtained andconsideredas reference. Thesemarker trajectorieswereused to calculate the same variablesmeasured by theMIMU.Measured and simulated MIMU data were then combined, as detailed in Table 1, obtaining five different scenarios. Each signal combination was fed as an input of an Extended Kalman Filter (EKF) [2] to estimate the 3D MIMU orientation. The rationale was to assess, both individually and jointly, the effect of the errors associated to the EKFmodel, as well as to each MIMU sensor. The accuracy of the obtained orientations was then evaluated in terms of orientation error [2]. A Kruskal–Wallis testwithpost-hocDunncomparisons ( =0.05)was performed to investigate whether significance differences existed among the obtained orientation errors. Results: Results (mean one standard deviation) about the error contribution associated to the EKF model and to the MIMU sensors